Diffusion gradient temporal difference for cooperative reinforcement learning with linear function approximation

We introduce a diffusion-based algorithm in which multiple agents cooperate to predict a common and global state-value function by sharing local estimates and local gradient information among neighbors. Our algorithm is a fully distributed implementation of the gradient temporal difference with linear function approximation, to make it applicable to multiagent settings. Simulations illustrate the benefit of cooperation in learning, as made possible by the proposed algorithm.